17 Mar 2022
17 Mar 2022
Status: a revised version of this preprint is currently under review for the journal TC.

Homogeneity assessment of Swiss snow depth series: Comparison of break detection capabilities of (semi-) automatic homogenisation methods

Moritz Buchmann1,2,3, John Coll4, Johannes Aschauer1, Michael Begert5, Stefan Brönnimann2,3, Barbara Chimani6, Gernot Resch7, Wolfgang Schöner7, and Christoph Marty1 Moritz Buchmann et al.
  • 1WSL Institute for Snow and Avalanche Research SLF, Davos, Switzerland
  • 2Oeschger Centre for Climate Change Research, University of Bern, Bern, Switzerland
  • 3Institute of Geography, University of Bern, Bern, Switzerland
  • 4CGG, Crawley, United Kingdom
  • 5Federal Office of Meteorology and Climatology (MeteoSwiss), Zurich Airport, Switzerland
  • 6Zentralanstalt für Meteorologie und Geodynamik (ZAMG), Vienna, Austria
  • 7Institute of Geography, University of Graz, Austria

Abstract. Knowledge concerning possible inhomogeneities in a data set is of key importance for any subsequent climatological analyses. Well-established relative homogenization methods developed for temperature and precipitation exist, but with only little experience for snow. We undertook a homogeneity assessment of Swiss snow depth series by running and comparing the results from three well-established semi-automatic break point detection methods (ACMANT, Climatol, and HOMER). Break points identified by each method allowed us to compare the results of the different methods, and by only treating break points as valid if detected in reasonably close proximity by at least two methods, we increased the robustness of the results. We investigated 184 series, of various length between 1930 and 2021 and ranging from 200 to 2500 m a.s.l. and found 45 valid break points. Of those 45, 71 % could be attributed to relocations or observer changes. Metadata are helpful, but not sufficient for break point verification as more than 90 % of recorded events did not lead to valid break points. Using such a combined approach (2 out of 3 methods) is highly beneficial, as it increases the confidence in identified break points in contrast to any single method, with or without metadata.

Moritz Buchmann et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on tc-2022-48', Ross Brown, 24 Mar 2022
    • AC1: 'Reply on RC1', Moritz Buchmann, 20 Apr 2022
  • RC2: 'Comment on tc-2022-48', Anonymous Referee #2, 26 Apr 2022
    • AC2: 'Reply on RC2', Moritz Buchmann, 04 May 2022
  • RC3: 'Comment on tc-2022-48', Anonymous Referee #3, 04 May 2022
    • AC3: 'Reply on RC3', Moritz Buchmann, 11 May 2022

Moritz Buchmann et al.

Data sets

Input data for break detection of Swiss snow depth series Moritz Buchmann, Johannes Aschauer, Michael Begert, Christoph Marty

Moritz Buchmann et al.


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Short summary
Knowledge about inhomogeneities in a data set are important for any subsequent climatological analysis. We ran three well-established homogenisation methods and compared the identified break points. By only treating breaks as valid when detected by at least 2 out of 3 methods,, we enhanced the robustness of our results. We found 45 breaks within 184 investigated series, of these 71 % could be explained by events recorded in the station history.